https://github.com/lukicdarkoo/cv-car-control

https://github.com/lukicdarkoo/cv-car-control

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• Host: GitHub
• URL: https://github.com/lukicdarkoo/cv-car-control
• Owner: lukicdarkoo
• Created: 2020-08-15T22:56:23.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-11-28T13:45:52.000Z (almost 4 years ago)
• Last Synced: 2023-07-31T12:14:52.863Z (over 1 year ago)
• Language: Python
• Size: 874 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

## Quick Start

#### Dependencies

Here is the list of dependcies:

- [Webots](https://www.cyberbotics.com/)

- [OpenCV for Python](https://pypi.org/project/opencv-python/)

In case you use Ubuntu or similar, run:

```bash

sudo snap install webots

pip3 install opencv-contrib-python

```

#### Project Files

Clone the project as:

```bash

git clone --depth=1 https://github.com/lukicdarkoo/cv-car-control.git

```

> Make sure you have [`git lfs`](https://git-lfs.github.com/) installed if you want to get pre-trained model as well.

#### Simulation

Standalone fist detector (without simulation) is located in `./controllers/steering_wheel_controller/wheel_detector.py` and you can run it as:

```bash

python3 ./controllers/steering_wheel_controller/wheel_detector.py

```

However, if you want to have a fun and run the simulation, execute the following command:

```bash

webots ./worlds/city.wbt

```

(or simply open `./worlds/city.wbt` in Webots GUI)

## Training

If you want to improve the model provided within this project, here is an explanation on how to do it.

### Make Dataset

Install `labelImg` and open it:

```bash

pip3 install labelImg

labelImg

```

In `labelImg` make sure to click `YOLO` and annotate all the images.

Once all the images annotated move them, together with the corresponding `*.txt` files, to `dataset/train` and `dataset/valid`.

It is very important to move 10-20 percent of the images to `dataset/valid` directory and the rest, move to `dataset/train`.

YOLO also needs a list of images for validation and training written to `*.txt` files.

You can make the files by invoking the following commands from the project's root:

```bash

cd ./dataset

find train -name '*.jpg' > train.txt

find valid -name '*.jpg' > valid.txt

```

### Train Locally

If you have GPU and CUDA available then you may prefer to train the YOLO locally.

First, install `darknet` with GPU and CUDA support:

```bash

git clone https://github.com/pjreddie/darknet

cd darknet

sed -i 's/CUDNN=0/CUDNN=1/g' darknet/Makefile

sed -i 's/GPU=0/GPU=1/g' darknet/Makefile

make -j8

```

To accelerate training it is usually a good idea to start from pre-trained model.

Download the pre-trained model in the project directory:

```bash

wget https://pjreddie.com/media/files/darknet53.conv.74

```

Finally, you can initiate the training as:

```bash

cd ./dataset

$HOME/darknet/darknet detector train ./custom.data ../yolov3-tiny_train.cfg ../darknet53.conv.74 

```

and your models will be available in `./dataset/backup`.

### Train on Google Colab

[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lukicdarkoo/cv-car-control/blob/master/TrainYOLOFistDetector.ipynb)  

In case you don't have GPU it probably a good idea to use Google Colab (click on the badge above).

After the Google Colab is ready, upload `yolov3-tiny_train.cfg` and `./dataset` folder compressed as `dataset.tar.xz`.

> You can create `dataset.tar.xz` as `tar -cf ./dataset ./dataset.tar.xz`

Run all cells and after approx. 1 hour of training the model will be ready waiting in `./dataset/backup`.